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Structural damage identification with output-only strain measurements and swarm intelligence algorithms: a comparative study

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第5期35卷 056125-056125页

作者： Zhang, Guangcai Hou, Jiale Feng, Kun Wan, Chunfeng Xie, Liyu Xue, Songtao Noori, Mohammad Ding, Zhenghao Southeast Univ Key Lab Concrete & Prestressed Concrete Struct Minist Educ Nanjing Peoples R China Tsinghua Univ Dept Civil Engn Beijing Peoples R China Queens Univ Belfast Sch Nat & Built Environm Belfast North Ireland Queens Univ Belfast Sch Mech & Aerosp Engn Belfast North Ireland Tongji Univ Dept Disaster Mitigat Struct Shanghai Peoples R China Tohoku Inst Technol Dept Architecture Sendai Japan Calif Polytech State Univ Dept Polit Arcata CA USA Univ Leeds Sch Civil Engn Leeds England Kyoto Univ Kyoto Japan

The identification of structural damage with the unavailability of input excitations is highly desired but challenging since structural dynamic responses are affected by the coupling effect of structural parameters and external excitations. To deal with this issue, in this paper, an output-only damage identification strategy based on swarm intelligence algorithms and correlation functions of strain responses is proposed to identify structures subjected to single or multiple unknown white noise excitations. In the proposed strategy, four different population-based optimization algorithms-particle swarm optimization, the butterfly optimization algorithm, the tree seed algorithm, and a micro search Jaya (MS-Jaya)-are employed and compared. The micro search mechanism is integrated into a basic Jaya algorithm to improve its computational efficiency and accuracy by eliminating some damage variables with small values for the identified best solution after several iterations. The objective function is established based on the proposed auto/cross-correlation function of strain responses and a penalty function. The effectiveness of the proposed method is verified with numerical studies on a simply supported beam structure and a steel grid benchmark structure under ambient excitation. In addition, the effect of the reference point, number of sensors, and arrangement of strain gauges on the performance of the proposed method are discussed in detail. The investigated results demonstrate that the proposed approach can accurately detect, locate, and quantify structural damage with limited sensors and 20% noise-polluted strain responses. In particular, the proposed MS-Jaya algorithm presents a more superior capacity in solving the optimization-based damage identification problem than the other three algorithms.

关键词： damage identification swarm intelligence algorithms Jaya algorithm strain responses correlation function micro search mechanism

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Financial sequence prediction based on swarm intelligence algorithms and internet of things

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JOURNAL OF SUPERCOMPUTING 2022年第15期78卷 17470-17490页

作者： Gao, Zheng Zhang, Chenxiang Li, Zhengyin Shandong Agr Univ Sch Econ & Management Tai An 271000 Shandong Peoples R China Univ Illinois Dept Ind & Enterprise Syst Engn Champaign IL 61820 USA Chinese Acad Agr Sci Inst Agr Econ & Dev Beijing 100081 Peoples R China

This paper aims to predict the financial time series. swarm intelligence algorithms usually use metadata to ensure objectivity without the statistical assumptions of the data. This paper proposed a prediction algorithm integrating multiple support vector regression (SVR) models. The algorithm selects different datasets to train these SVR models. This algorithm also adopts reasonable weights to combine the forecasting results of multiple models to reduce the overall prediction error. The weight of each model is dynamically adjusted according to its recent prediction accuracy. Therefore, this algorithm is adaptive and can deal with nonstationary problems. Five international authoritative stock indexes are used to compare the hybrid SVR model with a single SVR model for performance validation from the perspectives of normalized mean squared error, weighted directional symmetry, and root mean squared error. The results demonstrate that the hybrid SVR model has significantly improved the prediction accuracy and generalization ability of the prediction algorithm compared with a single SVR model. It reveals that selecting the appropriate input vector can achieve an excellent prediction effect.

关键词： Financial time series swarm intelligence algorithms Stock prediction Fuzzy theory

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Parallelization of swarm intelligence algorithms: Literature Review

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INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING 2022年第5-6期50卷 486-514页

作者： Menezes, Breno Augusto de Melo Kuchen, Herbert de Lima Neto, Fernando Buarque Univ Munster Dept Informat Syst Leonardo Campus 3 D-48149 Munster NRW Germany Univ Pernambuco ECOMP Rua Benf 455 BR-50720001 Recife PE Brazil

swarm intelligence (SI) algorithms are frequently applied to tackle complex optimization problems. SI is especially used when good solutions are requested for NP hard problems within a reasonable response time. And when such problems possess a very high dimensionality, a dynamic nature, or present intrinsic complex intertwined independent variables, computational costs for SI algorithms may still be too high. Therefore, new approaches and hardware support are needed to speed up processing. Nowadays, with the popularization of GPU and multi-core processing, parallel versions of SI algorithms can provide the required performance on those though problems. This paper aims to describe the state of the art of such approaches, to summarize the key points addressed, and also to identify the research gaps that could be addressed better. The scope of this review considers recent papers mainly focusing on parallel implementations of the most frequently used SI algorithms. The use of nested parallelism is of particular interest, since one level of parallelism is often not sufficient to exploit the computational power of contemporary parallel hardware. The sources were main scientific databases and filtered accordingly to the set requirements of this literature review.

关键词： Metaheuristics swarm intelligence algorithms Parallel computing Cluster computing High-performance computing

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Intelligent Management of Preparations for Programming Olympiads on the Basis of swarm intelligence algorithms

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SCIENTIFIC AND TECHNICAL INFORMATION PROCESSING 2022年第5期49卷 301-309页

作者： Borisov, V. V. Yanukovich, S. P. Mrochek, T. V. Vorob'ev, M. V. Mirankov, A. Yu. Natl Res Univ Moscow Power Engn Inst Moscow 111250 Russia OAO Mogilev Reg Dev Agcy Mogilev 212030 BELARUS Belarusian Russian Univ Mogilev 212000 BELARUS IOOO Epam Syst Mogilev 212030 BELARUS

As a rule, approaches to managing the preparation of learners for programming Olympiads are not intended for intelligent interactive support from learning management and the software tools based on these approaches do not allow efficient processing of information for shaping individual and team preparation trajectories and providing this information to learners. This study proposes a method of organizing the intelligent management of preparation for programming Olympiads, based on the system-level approach, organizational systems control theory, and implementation of the entire IT specialist training cycle, including the phases of planning, organization, control, and motivation. It is proposed to adaptively shape learning trajectories and adjust them on the basis of swarm intelligence algorithms modified to include features for managing the training of learners for programming Olympiads. It is proposed to shape individual learning trajectories for participants in individual Olympiads on the basis of a modified firefly algorithm and team learning trajectories for participants in team Olympiads on the basis of a modified fish school search algorithm. It is also proposed to supplement the training process with recommendations and exercises selected by the results of psychological testing and intended to develop the personal and psychological qualities of the learners. The recommendations are followed throughout the preparation process until the threshold values of personal and psychological qualities necessary for participation in an Olympiad are reached.

关键词： Olympiads programming contest swarm intelligence algorithms modified firefly and fish school search algorithms online judge systems individual and team learning trajectories

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Multi-objective scheduling of relief logistics based on swarm intelligence algorithms and spatio-temporal traffic flow

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Journal of Safety Science and Resilience 2021年第4期2卷 222-229页

作者： Zhiming Ding Zilin Zhao Detian Liu Yang Cao Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Institute of Software Chinese Academy of ScienceBeijing 100190China School of Information Beijing Wuzi UniversityBeijing 101149China

Emergency supplies scheduling needs to consider the state of the demanders,and reasonably scheduling and resource allocation are the heart of efficient *** rescue time,scheduling cost and demanders’satisfac-tion as goals,in this paper,an emergency supplies scheduling model based on multi-objective optimization was proposed to provide a wealth of decision-making *** four multi-objective optimization algorithms are employed to obtain the optimal set of scheduling *** addition,we design the minimum time cost model and the shortest route cost model by considering the change of the road network *** extensive simulation experiments are conducted on a real urban traffic *** experimental results show that the two cost models can serve different scheduling needs and provide efficient scheduling for emergency supplies.

关键词： Logistics scheduling MOP swarm intelligence algorithms Spatio-temporal trajectory Traffic flow

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swarm intelligence algorithms in Text Document Clustering with Various Benchmarks

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SENSORS 2021年第9期21卷 3196-3196页

作者： Selvaraj, Suganya Choi, Eunmi Kookmin Univ Dept Financial Informat Secur Seoul 02707 South Korea Kookmin Univ Dept Software Coll Comp Sci Seoul 02707 South Korea

Text document clustering refers to the unsupervised classification of textual documents into clusters based on content similarity and can be applied in applications such as search optimization and extracting hidden information from data generated by IoT sensors. swarm intelligence (SI) algorithms use stochastic and heuristic principles that include simple and unintelligent individuals that follow some simple rules to accomplish very complex tasks. By mapping features of problems to parameters of SI algorithms, SI algorithms can achieve solutions in a flexible, robust, decentralized, and self-organized manner. Compared to traditional clustering algorithms, these solving mechanisms make swarm algorithms suitable for resolving complex document clustering problems. However, each SI algorithm shows a different performance based on its own strengths and weaknesses. In this paper, to find the best performing SI algorithm in text document clustering, we performed a comparative study for the PSO, bat, grey wolf optimization (GWO), and K-means algorithms using six data sets of various sizes, which were created from BBC Sport news and 20 newsgroups. Based on our experimental results, we discuss the features of a document clustering problem with the nature of SI algorithms and conclude that the PSO and GWO SI algorithms are better than K-means, and among those algorithms, the PSO performs best in terms of finding the optimal solution.

关键词： swarm intelligence algorithms text document clustering artificial intelligence data mining

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Refining COVID-19 Lesion Segmentation in Lung CT Scans Using swarm intelligence and Evolutionary algorithms

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INTERNATIONAL JOURNAL OF MEDICAL ROBOTICS AND COMPUTER ASSISTED SURGERY 2025年第1期21卷 e70044页

作者： Gtifa, Wafa Fradi, Marwa Sakly, Anis Machhout, Mohsen Univ Monastir Monastir Natl Sch Engineers ENIM Lab Automat & Elect Syst & Environm Monastir Tunisia Univ Monastir Fac Sci Monastir Lab Elect & Microelect Monastir Tunisia

BackgroundAccurately identifying lung lesions in CT (Computed Tomography) scans remains crucial during the Coronavirus Disease 2019 (COVID-19) pandemic. swarm intelligence algorithms offer promising tools for this *** study compares four swarm intelligence algorithms Gravitational Search Algorithm (GSA), Bacterial Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), and Particle swarm Optimization (PSO) for segmenting COVID-19 lung ***, GSA, and BFOA achieved accuracies exceeding 90.5%, while the PSO algorithm further improved segmentation accuracy, reaching 91.45%, with an exceptional F1 score of 95.54%. Overall, the approach achieved up to 99% segmentation *** findings demonstrate the effectiveness of swarm and evolutionary algorithms in segmenting COVID-19 lesions, contributing to enhanced diagnostic accuracy and treatment efficiency.

关键词： BFOA computed tomography COVID-19 GA GSA PSO segmentation swarm intelligence algorithms

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Sparse signal reconstruction by swarm intelligence algorithms

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2021年第2期24卷 319-330页

作者： Erkoc, Murat Emre Karaboga, Nurhan Erciyes Univ Elect & Elect Eng Dep TR-38039 Kayseri Turkey

This study introduces a new technique for sparse signal reconstruction. In general, there are two classes of algorithms in the recovery of sparse signals: greedy approaches and l(1)-minimization methods. The proposed method employs swarm intelligence based techniques for sparse signal reconstruction. With this technique, the proposed method tries to find nonzero entries of a sparse signal. In addition, it uses least square method to obtain the magnitude of the reconstructed signal. In this study, artificial bee colony and particle swarm optimization algorithms are employed for the reconstruction of sparse signals. The algorithms are tested on some benchmark problems and empirical results for a number of test cases are obtained. In addition, the reconstruction performances of these two algorithms are compared with l(1) minimization, greedy algorithms and several lately announced methods. According to the results of the tests and comparisons, the proposed method for artificial bee colony and particle swarm optimization algorithms have better performance than the classical methods for some test cases and it can be used to recover sparse signals in general. Additionally, this method can be used to recover a sparse signal when classical methods fail to reconstruct the signal for some cases. (C) 2020 Karabuk University. Publishing services by Elsevier B.V.

关键词： Compressive sensing Sparse signal reconstruction swarm intelligence algorithms Artificial bee colony algorithm Particle swarm optimization

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A systematic review on software reliability prediction via swarm intelligence algorithms

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2024年第7期36卷

作者： Kong, Li Sheng Jasser, Muhammed Basheer Ajibade, Samuel-Soma M. Mohamed, Ali Wagdy Sunway Univ Sch Engn & Technol Dept Comp & Informat Syst 5Jalan Univ Bandar Sunway 47500 Selangor Darul Malaysia Sunway Univ Res Ctr Human Machine Collaborat HUMAC Sch Engn & Technol 5 Jalan Univ Bandar Sunway 47500 Selangor Darul Malaysia Istanbul Ticaret Univ Dept Comp Engn Istanbul Turkiye Cairo Univ Fac Grad Studies Stat Res Operat Res Dept Giza 12613 Egypt Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan

The widespread integration of software into all parts of our lives has led to the need for software of higher reliability. Ensuring reliable software usually necessitates some form of formal methods in the early stages of the development process which requires strenuous effort. Hence, researchers in the field of software reliability introduced Software Reliability Growth Models (SRGMs) as a relatively inexpensive approach to software reliability prediction. Conventional parameter estimation methods of SRGMs were ineffective and left more to be desired. Consequently, researchers sought out swarm intelligence to combat its flaws, resulting in significant improvements. While similar surveys exist within the domain, the surveys are broader in scope and do not cover many swarm intelligence algorithms. Moreover, the broader scope has resulted in the occasional omission of information regarding the design for reliability predictions. A more comprehensive survey containing 38 studies and 18 different swarm intelligence algorithms in the domain is presented. Each design proposed by the studies was systematically analyzed where relevant information including the measures used, datasets used, SRGMs used, and the effectiveness of each proposed design, were extracted and organized into tables and taxonomies to be able to identify the current trends within the domain. Some notable findings include the distance-based approach providing a high prediction accuracy and an increasing trend in hybridized variants of swarm intelligence algorithms designs to predict software reliability. Future researchers are encouraged to include Mean Square Error (MSE) or Root MSE as the measures offer the largest sample size for comparison.

关键词： swarm intelligence algorithms Software Reliability Prediction Software Reliability Growth Models Evolutionary algorithms

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Optimal energy management system for residential buildings considering the time of use price with swarm intelligence algorithms

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JOURNAL OF BUILDING ENGINEERING 2022年 59卷

作者： Yelisetti, Srinivas Saini, Vikash Kumar Kumar, Rajesh Lamba, Ravita Saxena, Akash MNIT Dept Elect Engn Jaipur 302017 India MNIT Ctr Energy & Environm Jaipur 302017 India Swami Keshvanand Inst Technol Management & Gramoth Dept Elect Engn Jaipur 302017 India

Revolutions in human activities and lifestyles result in a transition from conventional to intelligent residential building infrastructure. Conventional heating, ventilation, and air con-ditioning (HVAC), refrigerator and lighting system challenges are addressed without taking into account building heat gains, outdoor illuminance, and temperature. Based on these param-eters, a mathematical model for cost estimation of residential building energy consumption, considering indoor heat gains, outdoor temperature, outdoor illuminance, and TOU price has been developed. A total of 46 swarm intelligence based optimization algorithms are used to optimize different building parameters. These swarm intelligence algorithms (SIA) are compared using the convergence curves, statistical and box-plot analysis and the Bald Eagle search (BES) algorithm is found to be the best algorithm among all 46 SIAs. The mean energy consumption costs of the best algorithm, BES, and the worst algorithm, fireworks algorithm (FA) are found to be Rs. 8.85 and Rs. 12.98, respectively. In addition, economic analysis has been conducted for the proposed study and it is compared with the existing models with building energy management systems (BEMS) and conventional model (without BEMS). It is observed that, based on this analysis, the cost savings achieved by the proposed study are nearer to 34% and 57% as compared to existing and conventional models.

关键词： Building energy management system Heating ventilation and air conditioning system Lighting system swarm intelligence algorithms

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